888 search results - page 22 / 178 » Learning to Select a Ranking Function |
CIKM
2010
Springer
13 years 6 months ago
2010
Springer
Traditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance of documents for a given query. Although such a batch-learni...
IR
2010
13 years 6 months ago
2010
LETOR is a benchmark collection for the research on learning to rank for information retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the L...
SAC
2008
ACM
13 years 7 months ago
2008
ACM
When developing semantic applications, the construction of ontologies is a crucial part. We are developing a semiautomatic ontology construction approach, OntoCase, relying on ont...
CIKM
2009
Springer
14 years 2 months ago
2009
Springer
Traditional boosting algorithms for the ranking problems usually employ the pairwise approach and convert the document rating preference into a binary-value label, like RankBoost....
KDD
2007
ACM
14 years 8 months ago
2007
ACM
We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. ...